| Type: | Package |
| Title: | Create Volcano Plots for Differential Gene Expression Data |
| Version: | 0.1.0 |
| Description: | Provides functionality to create customizable volcano plots for visualizing differential gene expression analysis results. The package offers options to highlight genes of interest, adjust significance thresholds, customize colors, and add informative labels. Designed specifically for RNA-seq data analysis workflows. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | dplyr, ggplot2, ggrepel, ggtext, gridExtra, grid |
| RoxygenNote: | 7.3.3 |
| Depends: | R (≥ 3.5.0) |
| NeedsCompilation: | no |
| Packaged: | 2026-01-11 12:24:09 UTC; theodosiou |
| Author: | Loukas Theodosiou 
[aut, cre] |
| Maintainer: | Loukas Theodosiou <theodosiou@evolbio.mpg.de> |
| Repository: | CRAN |
| Date/Publication: | 2026-01-16 11:00:08 UTC |
Gene Expression Analysis Dataset
Description
A dataset containing gene expression analysis results. It has seven columns capturing various statistics related to gene expression.
Usage
all_genes
Format
A data frame with the following columns:
- genes
Character. Gene name or identifier.
- baseMean
Numeric. The base mean value for the gene across samples.
- log2FoldChange
Numeric. The log2 fold change of gene expression. Positive values indicate upregulation and negative values indicate downregulation.
- lfcSE
Numeric. Standard error of the log2 fold change.
- stat
Numeric. The Wald statistic for the gene's expression change.
- pvalue
Numeric. Raw p-value for the test of the gene's expression change.
- padj
Numeric. Adjusted p-value for multiple testing corrections.
Attention Genes Dataset
Description
A dataset containing specific genes of interest referred to as "attention genes".
Usage
attention_genes
Format
A data frame with 10 rows and 7 variables:
- genes
Character. Gene name or identifier.
- baseMean
Numeric vector: Base mean expression level of genes
- log2FoldChange
Numeric vector: Log2 Fold Change of gene expression
- lfcSE
Numeric vector: Standard error for log2 fold change
- stat
Numeric vector: Wald statistic for the gene's expression change
- pvalue
Numeric vector: Raw p-value from Wald test
- padj
Numeric vector: Adjusted p-value for multiple testing using the Benjamini-Hochberg procedure
Combine a ggplot Object with a Table of Genes
Description
This function takes a ggplot object and a data frame of gene details and produces a combined plot where the ggplot object is stacked above a table of gene details.
Usage
genes_table(plot_obj, data2)
Arguments
plot_obj |
A ggplot object, typically the output of a plotting function. |
data2 |
A data frame containing gene details. It should have columns named "genes", "baseMean", "log2FoldChange", "pvalue", and "padj". |
Value
A gtable object showing the ggplot stacked above a table of gene details.
Examples
# Load example datasets
data(all_genes)
data(attention_genes)
# Create a volcano plot highlighting genes of interest
plot <- ggvolc(all_genes, attention_genes, add_seg = TRUE)
# Combine the plot with a table showing gene statistics
# The table includes: gene names, baseMean, log2FoldChange, pvalue, and padj
genes_table(plot, attention_genes)
Create a Volcano Plot
Description
This function creates a volcano plot using ggplot2 based on provided datasets. It is particularly useful for visualizing differential gene expression data.
Usage
ggvolc(
data1,
data2 = NULL,
size_var = NULL,
p_value = 0.05,
fc = 1,
not_sig_color = "#808080",
down_reg_color = "#00798c",
up_reg_color = "#d1495b",
add_seg = FALSE
)
Arguments
data1 |
Data frame for the primary dataset. |
data2 |
Data frame for the secondary dataset. Default is NULL. |
size_var |
Variable for determining the size of points. Options are "log2FoldChange" and "pvalue". Default is "log2FoldChange". |
p_value |
Threshold for statistical significance. Default is 0.05. |
fc |
Fold change threshold for determining upregulated or downregulated genes. Default is 1. |
not_sig_color |
Color for non-significant genes. Default is "grey82". |
down_reg_color |
Color for downregulated genes. Default is "#00798c". |
up_reg_color |
Color for upregulated genes. Default is "#d1495b". |
add_seg |
Logical. If TRUE, dashed lines will be added to the plot indicating the p-value and fold change thresholds. Default is FALSE. |
Value
A ggplot2 object displaying the volcano plot.
Examples
# Load example datasets included in the package
data(all_genes)
data(attention_genes)
# Create a basic volcano plot with default settings
# Points are colored by significance (p < 0.05, |log2FC| > 1)
ggvolc(all_genes)
# Highlight specific genes of interest with labels
# These genes are shown with black borders and gene names
ggvolc(all_genes, attention_genes)
# Add dashed lines to indicate significance thresholds
ggvolc(all_genes, attention_genes, add_seg = TRUE)
# Customize colors for up- and down-regulated genes
ggvolc(all_genes, attention_genes,
up_reg_color = "#E63946",
down_reg_color = "#457B9D")
# Scale point size by p-value instead of default
ggvolc(all_genes, attention_genes, size_var = "pvalue")
# Adjust significance thresholds (p-value and fold change)
ggvolc(all_genes, p_value = 0.01, fc = 2)
Print Welcome Message and ASCII Art upon Loading 'ggvolc'
Description
This function is executed when the 'ggvolc' package is attached to the R session. It prints a welcome message and an ASCII representation related to the package.
See Also
Examples
# The function is automatically called when you use:
# library(ggvolc)